MLWiNS: Quality Versus Quantity in Spectrum Sensing with Distributed Sensors

MLWiNS：分布式传感器频谱传感的质量与数量

• 批准号: 2002921
• 负责人: Bertrand Hochwald
• 金额: $ 44.33万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2002921&HistoricalAwards=false
• 关键词: MLWiNS; Quality; Versus; Quantity; Spectrum

An accurate map showing usage of the wireless spectrum can be used to inform spectrum policy, determine usage patterns and situational awareness, and evaluate the feasibility of opportunistic spectrum access methods indoors and outdoors, especially in the crowded sub-6 GHz spectrum. However, the success of creating such a map depends on the quality and quantity of sensors measuring spectrum activity, and how their measurements are processed by a centralized server. In general, because the sensors operate in an unknown or time-varying environment, many distributed sensors are generally needed to compensate for this uncertainty. As a result, the cost, power consumption, and size of the sensors need to be very low to allow wide-scale deployment. This project examines the sensor quality versus quantity tradeoff for a large-scale distributed measurement system that infers wireless activity throughout a region. Knowledge of the activity of the spectrum is hampered by the lack of accurate real-time measurements on a truly large scale and deploying a large number of sensors is hampered by cost, power, and complexity. The research addresses the important aspect of how to commoditize the hardware and drive the cost and power down by an order of magnitude, thus potentially paving the way to large spectrum data sets. The work will be used to seed projects at an existing Research Experience for Undergraduates (REU) site at the University of Notre Dame. The results will be disseminated to the research community to encourage discussions about how spectral occupancy can be measured reliably in a low-cost and scalable manner.A spectrum sensor is, at its heart, a power detector for frequency bands of interest. Traditionally, such a sensor is evaluated as a radio since it generally tunes and down-converts its input to an intermediate-frequency or baseband signal, digitizes, filters, and then analyzes the power spectrum. The standard performance specifications of a radio include sensitivity, linearity, bandwidth, image-rejection, frequency stability, phase noise, and dynamic range. Meeting stringent requirements on these specifications contributes directly to cost and power consumption. However, spectrum mapping system performance is also determined by the ability of the centralized server to infer activities over a whole region from a widely distributed set of sensors. This research focuses on relaxing many sensor requirements, and analyzing how a real-time spectrum mapping system that uses machine-learning performs when the number of sensors is increased to compensate for the reduced capability per sensor. The effort seeks to show that there is an advantageous trade-off of quantity over quality---the information learned from additional sensors in a large-scale deployment easily compensates for their reduced individual capabilities, especially when accounting for cost and power consumption. Implications for computational and network load requirements and sensor size are also considered. This research effort is an innovative whole-system approach to determining the quality-quantity contour for distributed spectrum measurements. The effort will: (i) establish models, metrics, and fidelity criteria for the performance of a machine-learning radio-frequency spectrum sensing system with distributed sensors; (ii) examine opportunities to dramatically reduce sensor cost and power consumption; (iii) build an extensive test and measurement platform of sensors to validate the assumptions, analyses, methods and conclusions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

显示无线频谱使用情况的准确地图可用于告知频谱政策，确定使用模式和态势感知，并评估室内和室外机会性频谱接入方法的可行性，特别是在拥挤的6 GHz以下频谱中。 然而，创建这样的地图的成功取决于测量频谱活动的传感器的质量和数量，以及中央服务器如何处理它们的测量结果。 通常，由于传感器工作在未知或时变的环境中，因此通常需要许多分布式传感器来补偿这种不确定性。 因此，传感器的成本、功耗和尺寸需要非常低，以允许大规模部署。 这个项目研究了传感器的质量与数量的权衡，为大规模的分布式测量系统，推断整个地区的无线活动。由于缺乏真正大规模的精确实时测量，对频谱活动的了解受到阻碍，并且部署大量传感器受到成本，功率和复杂性的阻碍。 该研究解决了如何将硬件商品化并将成本和功耗降低一个数量级的重要方面，从而可能为大型频谱数据集铺平道路。 这项工作将用于在圣母大学现有的本科生研究经验（REU）网站上播种项目。 研究结果将分发给研究界，以鼓励讨论如何以低成本和可扩展的方式可靠地测量频谱占用率。频谱传感器的核心是感兴趣频带的功率检测器。传统上，这种传感器被评估为无线电，因为它通常将其输入调谐和下变频为中频或基带信号，数字化，滤波，然后分析功率谱。 无线电的标准性能指标包括灵敏度、线性度、带宽、镜像抑制、频率稳定性、相位噪声和动态范围。 满足这些规范的严格要求直接导致成本和功耗。 然而，频谱映射系统的性能还取决于集中式服务器从广泛分布的传感器集合推断整个区域上的活动的能力。本研究的重点是放宽许多传感器的要求，并分析如何使用机器学习的实时频谱映射系统时，传感器的数量增加，以补偿每个传感器的能力降低执行。 这项工作旨在表明，在质量上有一个有利的数量权衡-从大规模部署中的额外传感器获得的信息很容易补偿其降低的个人能力，特别是在考虑成本和功耗时。计算和网络负载的要求和传感器的大小的影响也被考虑。这项研究工作是一种创新的全系统方法来确定分布式频谱测量的质量-数量轮廓。 这项工作将：（i）为具有分布式传感器的机器学习射频频谱感测系统的性能建立模型、度量和保真度标准;（ii）研究大幅降低传感器成本和功耗的机会;（iii）建立一个广泛的传感器测试和测量平台，以验证假设，分析，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

LSTMs for Keyword Spotting with ReRAM-based Compute-In-Memory Architectures

使用基于 ReRAM 的内存计算架构进行关键字识别的 LSTM

• DOI: 10.1109/iscas51556.2021.9401295
• 发表时间: 2021
• 期刊: 2021 IEEE International Symposium on Circuits and Systems
• 作者: Schaefer, Clemens JS;Horeni, Mark;Taheri, Pooria;Joshi, Siddharth
• 通讯作者: Joshi, Siddharth

Analog vs. Digital Spatial Transforms: A Throughput, Power, and Area Comparison

模拟与数字空间变换：吞吐量、功耗和面积比较

• DOI: 10.1109/mwscas48704.2020.9184566
• 发表时间: 2020
• 期刊: IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS
• 作者: Enciso, Zephan M.;Hadi Mirfarshbafan, Seyed;Castaneda, Oscar;Schaefer, Clemens JS.;Studer, Christoph;Joshi, Siddharth
• 通讯作者: Joshi, Siddharth

Robust Neural Network-Based Spectrum Occupancy Mapping

基于鲁棒神经网络的频谱占用映射

• DOI: 10.1109/dyspan53946.2021.9677439
• 发表时间: 2021
• 期刊: 2021 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN
• 作者: Termos, Abbas;Hochwald, Bertrand
• 通讯作者: Hochwald, Bertrand